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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Lesser General Public
License as published by the Free Software Foundation; either
version 2.1 of the License, or (at your option) any later version.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Library General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#include "public.h"
#include "clustererKM.h"
using namespace std;
ClustererKM::~ClustererKM()
{
DEL(kmeans);
}
void ClustererKM::Train(std::vector< fvec > samples)
{
if(!samples.size()) return;
int dim = samples[0].size();
if(!bIterative)
{
DEL(kmeans);
}
bool bInit = false;
if(kmeans && kmeans->GetClusters() != nbClusters) DEL(kmeans);
if(!kmeans)
{
bInit = true;
kmeans = new KMeansCluster(nbClusters);
kmeans->AddPoints(samples);
kmeans->SetPlusPlus(kmeansPlusPlus);
kmeans->InitClusters();
}
kmeans->SetSoft(bSoft);
kmeans->SetGMM(bGmm);
kmeans->SetBeta(beta);
kmeans->SetPower(power);
kmeans->Update(bInit);
if(!bIterative)
{
int iterations = 20;
FOR(i, iterations) kmeans->Update();
}
}
fvec ClustererKM::Test( const fvec &sample)
{
fvec res;
res.resize(nbClusters,0);
if(!kmeans) return res;
kmeans->Test(sample, res);
float sum = 0;
FOR(i, res.size()) sum += res[i];
FOR(i, res.size()) res[i] /= sum;
return res;
}
fvec ClustererKM::Test( const fVec &sample)
{
fvec res;
res.resize(nbClusters,0);
if(!kmeans) return res;
kmeans->Test(sample, res);
float sum = 0;
FOR(i, res.size()) sum += res[i];
FOR(i, res.size()) res[i] /= sum;
return res;
}
void ClustererKM::SetParams(u32 clusters, int method, float beta, int power, bool kmeansPlusPlus)
{
this->nbClusters = clusters;
this->beta = beta;
this->power = power;
this->kmeansPlusPlus = kmeansPlusPlus;
switch(method)
{
case 0:
this->bSoft = false;
this->bGmm = false;
break;
case 1:
this->bSoft = true;
this->bGmm = false;
break;
case 2:
this->bSoft = false;
this->bGmm = true;
break;
}
}
const char *ClustererKM::GetInfoString()
{
char *text = new char[1024];
sprintf(text, "K-Means\n");
sprintf(text, "%sClusters: %d\n", text, nbClusters);
sprintf(text, "%sType:", text);
if(!bSoft && !bGmm) sprintf(text, "%sK-Means (plusplus: %i)\n", text, kmeansPlusPlus);
else if(bSoft) sprintf(text, "%sSoft K-Means (beta: %.3f, plusplus: %i)\n", text, beta, kmeansPlusPlus);
else sprintf(text, "%sGMM\n", text);
sprintf(text, "%sMetric: ", text);
switch(power)
{
case 0:
sprintf(text, "%sinfinite norm\n", text);
break;
case 1:
sprintf(text, "%s1-norm (Manhattan)\n", text);
break;
case 2:
sprintf(text, "%s2-norm (Euclidean)\n", text);
break;
default:
sprintf(text, "%s%d-norm\n", text, power);
break;
}
return text;
}
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